21 citations · 27 across the 4 of their papers we have counts for
4 papers
Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models
Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton +1
Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned fro…
Characteristic Kernels and Infinitely Divisible Distributions
Yu Nishiyama, Kenji Fukumizu
We connect shift-invariant characteristic kernels to infinitely divisible distributions on . Characteristic kernels play an important role in machine learning appli…
Filtering with State-Observation Examples via Kernel Monte Carlo Filter
Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton +1
This paper addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e. the observation m…
Hilbert Space Embeddings of POMDPs
Yu Nishiyama, Abdeslam Boularias, Arthur Gretton +1
A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature space…